22 The devil is in the details Total order to serialization order Provide various levels of isolation / atomicity degrees Read operations always local Propagate changes on transaction basis No 2PC Rely on delivery guarantees Return to user once local replica commits Determinism Propagate changes vs. SQL statements

23 Distributed locking Response Time in ms Did we really avoided the dangers of replication? Postgres- R Number Servers Response Time in ms Distributed locking Number Servers Postrges-R removed a lot of the overhead of replication, providing scalability while maintaining strong consistency 24

24 In perspective

25 What worked Ordered and guaranteed propagation of changes through an agreement protocol external to the engine The implementation was crucial to prove the point Thinking through the optimizations / real system issues Levels of consistency

26 What did not work Modify the engine Today middleware based solutions Enforce serializability Today SI and session consistency Data warehousing less demanding Cloud computing has lowered the bar

38 Understanding the full picture Paxos Group communication protocols differ in the exact properties they provide Often difficult to understand for outsiders Subtleties in implementation and efficiency Complex implementations Adjusting the agreement protocols to the needs of databases Properties that suffice Efficient implementation Plenty of use cases (one size does not fit all)

39 Database and distributed systems Databases and distributed systems have converged in practice Many similar concepts Research Work still done in separate communities Teaching Dire need for joint courses (thanks to Amr El Abbadi and Divy Agrawal from UCSB!)

40 Thanks Andre Schiper, Fernando Pedone, Matthias Wiesmann (EPFL) Marta Patiño, Ricardo Jiménez (UPM) The PostgreSQL community PhD and master students at ETH and McGill who have worked and are working on related ideas Many colleagues and friends...

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